Fix: Runtime error

app.py

@@ -58,58 +58,58 @@ def main():
             """
         )
 
-    # #############################################################################
-    # ################################ GradCam Tab ################################
-    # #############################################################################
-    with gr.Tab("GradCam"):
-        gr.Markdown(
-            """
-            Visualize Class Activations Maps generated by the model's layer for the predicted class.
-            This is used to see what the model is actually looking at in the image.
-            """
-        )
-        with gr.Row():
-            # Update the image input dimensions
-            img_input = [gr.Image(label="Input Image", type="numpy", height=224)]  # Changed dimensions
-            gradcam_outputs = [
-                gr.Label(label="Predictions"),
-                gr.Image(label="GradCAM Output", height=224)  # Match input image height
-            ]
-
-        with gr.Row():
-            gradcam_inputs = [
-                gr.Slider(0, 1, value=0.5, label="Activation Map Transparency"),
-                gr.Slider(1, 10, value=3, step=1, label="Number of Top Predictions"),
-                gr.Slider(1, 6, value=4, step=1, label="Target Layer Number")
-            ]
-
-        gradcam_button = gr.Button("Generate GradCAM")
-        # Pass model to inference function using partial
-        from functools import partial
-        inference_fn = partial(inference, model=model, classes=classes)
-        gradcam_button.click(inference_fn, inputs=img_input + gradcam_inputs, outputs=gradcam_outputs)
-
-        gr.Markdown("## Examples")
-        gr.Examples(
-            examples=[
-                ["./assets/examples/dog.jpg", 0.5, 3, 4],
-                ["./assets/examples/cat.jpg", 0.5, 3, 4],
-                ["./assets/examples/frog.jpg", 0.5, 3, 4],
-                ["./assets/examples/bird.jpg", 0.5, 3, 4],
-                ["./assets/examples/shark-plane.jpg", 0.5, 3, 4],
-                ["./assets/examples/car.jpg", 0.5, 3, 4],
-                ["./assets/examples/truck.jpg", 0.5, 3, 4],
-                ["./assets/examples/horse.jpg", 0.5, 3, 4],
-                ["./assets/examples/plane.jpg", 0.5, 3, 4],
-                ["./assets/examples/ship.png", 0.5, 3, 4]
-            ],
-            inputs=img_input + gradcam_inputs,
-            fn=inference_fn,
-            outputs=gradcam_outputs
-        )
-
-    gr.close_all()
-    demo.launch(debug=True)
+        # #############################################################################
+        # ################################ GradCam Tab ################################
+        # #############################################################################
+        with gr.Tab("GradCam"):
+            gr.Markdown(
+                """
+                Visualize Class Activations Maps generated by the model's layer for the predicted class.
+                This is used to see what the model is actually looking at in the image.
+                """
+            )
+            with gr.Row():
+                img_input = [gr.Image(label="Input Image", type="numpy", height=224)]
+                gradcam_outputs = [
+                    gr.Label(label="Predictions"),
+                    gr.Image(label="GradCAM Output", height=224)
+                ]
+
+            with gr.Row():
+                gradcam_inputs = [
+                    gr.Slider(0, 1, value=0.5, label="Activation Map Transparency"),
+                    gr.Slider(1, 10, value=3, step=1, label="Number of Top Predictions"),
+                    gr.Slider(1, 6, value=4, step=1, label="Target Layer Number")
+                ]
+
+            gradcam_button = gr.Button("Generate GradCAM")
+            
+            # Pass model to inference function using partial
+            from functools import partial
+            inference_fn = partial(inference, model=model, classes=classes)
+            gradcam_button.click(inference_fn, inputs=img_input + gradcam_inputs, outputs=gradcam_outputs)
+
+            gr.Markdown("## Examples")
+            gr.Examples(
+                examples=[
+                    ["./assets/examples/dog.jpg", 0.5, 3, 4],
+                    ["./assets/examples/cat.jpg", 0.5, 3, 4],
+                    ["./assets/examples/frog.jpg", 0.5, 3, 4],
+                    ["./assets/examples/bird.jpg", 0.5, 3, 4],
+                    ["./assets/examples/shark-plane.jpg", 0.5, 3, 4],
+                    ["./assets/examples/car.jpg", 0.5, 3, 4],
+                    ["./assets/examples/truck.jpg", 0.5, 3, 4],
+                    ["./assets/examples/horse.jpg", 0.5, 3, 4],
+                    ["./assets/examples/plane.jpg", 0.5, 3, 4],
+                    ["./assets/examples/ship.png", 0.5, 3, 4]
+                ],
+                inputs=img_input + gradcam_inputs,
+                fn=inference_fn,
+                outputs=gradcam_outputs
+            )
+
+        # Launch the demo (moved inside the Blocks context)
+        demo.launch(debug=True)
 
 
 if __name__ == "__main__":